#!/usr/bin/python
# -*- coding: utf-8 -*-

import tensorflow as tf


class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.

    sequence_length:
        句子的长度。请注意，我们通过添加特殊标记，使得所欲的句子都拥有了相同的长度（我们的数据集是20）。
    num_classes:
        最后一层分类的数目，在这里我们是进行二分类（正向评论和负向评论）。
    vocab_size:
        词汇量的大小。这个参数是为了确定我们词向量嵌入层的大小，最终的总词向量维度是 [vocabulary_size, embedding_size] 。
    embedding_size:
        每个单词的词向量的长度128或者256。
    filter_sizes:
        这个参数确定我们希望我们的卷积核每次覆盖几个单词。对于每个卷积核，我们都将有 num_filters 个。比如，filter_sizes = [3, 4, 5] ,
        这就意味着，卷积核一共有三种类型，分别是每次覆盖3个单词的卷积核，每次覆盖4个单词的卷积核和每次覆盖5个单词的卷积核。
        卷积核一共的数量是 3 * num_filters 个。
    num_filters:
        每个卷积核的数量（参考 filter_sizes 参数的介绍）。
    """

    def __init__(self,
                 sequence_length,
                 num_classes,
                 vocab_size,
                 embedding_size,
                 filter_sizes,
                 num_filters,
                 pretrained_embedding,
                 l2_reg_lambda=0.0,
                 train_embedding=False):

        # Placeholders for input, output and dropout
        self.input_x = tf.placeholder(
            tf.int32, [None, sequence_length], name='input_x')  # (batch_size,16)
        self.input_y = tf.placeholder(
            tf.float32, [None, num_classes], name='input_y')  # (batch_size, 3)
        self.dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')

        # Keeping track of l2 regularization loss (optional)
        l2_loss = tf.constant(0.0)

        # Embedding layer
        with tf.device('/cpu:0'), tf.name_scope('embedding'):

            if pretrained_embedding is not None:
                self.Embedding = tf.get_variable(
                    'Embedding', initializer=pretrained_embedding, trainable=train_embedding)  # (8000,200)
            else:
                self.Embedding = tf.Variable(tf.random_uniform(
                    [vocab_size, embedding_size], -1.0, 1.0), name='Embedding')
            # 变成3维(batch_size,16,200)
            self.embedded_chars = tf.nn.embedding_lookup(self.Embedding, self.input_x)
            # 变成4维(batch_size,16,200,1)
            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

        # Create a convolution + maxpool layer for each filter size
        pooled_outputs = []
        for i, filter_size in enumerate(filter_sizes):
            with tf.name_scope('conv-maxpool-%s' % filter_size):
                # Convolution Layer (2,200,1,128)
                filter_shape = [filter_size, embedding_size, 1, num_filters]
                W = tf.Variable(tf.truncated_normal(
                    filter_shape, stddev=0.1), name='W')  # (2,200,1,num_filters)
                b = tf.Variable(tf.constant(
                    0.1, shape=[num_filters]), name='b')  # (num_filters,)
                conv = tf.nn.conv2d(
                    self.embedded_chars_expanded,
                    W,
                    strides=[1, 1, 1, 1],
                    padding='VALID',
                    name='conv')  # (?, 15, 1, num_filters)
                # Apply nonlinearity
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name='relu')
                # Maxpooling over the outputs
                pooled = tf.nn.max_pool(
                    h,
                    ksize=[1, sequence_length - filter_size + 1, 1, 1],
                    strides=[1, 1, 1, 1],
                    padding='VALID',
                    name='pool')
                pooled_outputs.append(pooled)

        # Combine all the pooled features
        num_filters_total = num_filters * len(filter_sizes)
        self.h_pool = tf.concat(pooled_outputs, 3)  # (?,1,1,768)
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])  # (?,768)

        # Add dropout
        with tf.name_scope('dropout'):
            self.h_drop = tf.nn.dropout(
                self.h_pool_flat, self.dropout_keep_prob)  # (?,768)

        # Final (unnormalized) scores and predictions
        with tf.name_scope('output'):
            W = tf.get_variable(
                'W',
                shape=[num_filters_total, num_classes],
                initializer=tf.contrib.layers.xavier_initializer())  # (763,3)
            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name='b')  # (3,)
            l2_loss += tf.nn.l2_loss(W)
            l2_loss += tf.nn.l2_loss(b)
            self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name='scores')  # (?,3)
            self.predictions = tf.argmax(self.scores, 1, name='predictions')

        # Calculate mean cross-entropy loss
        with tf.name_scope('loss'):
            losses = tf.nn.softmax_cross_entropy_with_logits(
                logits=self.scores, labels=self.input_y)
            self.closs = tf.reduce_mean(losses)
            self.rloss = l2_reg_lambda * l2_loss
            self.loss = self.closs + self.rloss

        # Accuracy
        with tf.name_scope('accuracy'):
            correct_predictions = tf.equal(
                self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(
                tf.cast(correct_predictions, 'float'), name='accuracy')
